import gradio as gr import pandas as pd import numpy as np import pickle from sklearn.preprocessing import StandardScaler import sklearn import os print(f"Prediction environment scikit-learn version: {sklearn.__version__}") def decode_file(file_path): with open(file_path, 'rb') as file: obj = pickle.load(file) return obj # Load the model without try/except model = decode_file('./model.pkl') def preprocess_input(data_dict): """Preprocess input data to match the training format""" # First create DataFrame with original columns df = pd.DataFrame([data_dict]) # Store original features before transformation original_df = df.copy() # Numeric features numeric_features = ['age', 'avg_glucose_level', 'bmi'] # Scale numeric features and add prefix scaler = StandardScaler() scaled_features = scaler.fit_transform(df[numeric_features]) for i, feat in enumerate(numeric_features): df[f'num__{feat}'] = scaled_features[:, i] # Create categorical features # Gender df['cat__gender_Male'] = (df['gender'] == 'Male').astype(float) df['cat__gender_Other'] = 0.0 # Hypertension df['cat__hypertension_1'] = df['hypertension'].astype(float) # Heart Disease df['cat__heart_disease_1'] = df['heart_disease'].astype(float) # Ever Married df['cat__ever_married_Yes'] = (df['ever_married'] == 'Yes').astype(float) # Work Type df['cat__work_type_Never_worked'] = (df['work_type'] == 'Never_worked').astype(float) df['cat__work_type_Private'] = (df['work_type'] == 'Private').astype(float) df['cat__work_type_Self-employed'] = (df['work_type'] == 'Self-employed').astype(float) df['cat__work_type_children'] = (df['work_type'] == 'children').astype(float) # Residence Type df['cat__Residence_type_Urban'] = (df['Residence_type'] == 'Urban').astype(float) # Smoking Status df['cat__smoking_status_formerly smoked'] = (df['smoking_status'] == 'formerly smoked').astype(float) df['cat__smoking_status_never smoked'] = (df['smoking_status'] == 'never smoked').astype(float) df['cat__smoking_status_smokes'] = (df['smoking_status'] == 'smokes').astype(float) # Select only the transformed columns in the correct order feature_columns = [ 'num__age', 'num__avg_glucose_level', 'num__bmi', 'cat__gender_Male', 'cat__gender_Other', 'cat__hypertension_1', 'cat__heart_disease_1', 'cat__ever_married_Yes', 'cat__work_type_Never_worked', 'cat__work_type_Private', 'cat__work_type_Self-employed', 'cat__work_type_children', 'cat__Residence_type_Urban', 'cat__smoking_status_formerly smoked', 'cat__smoking_status_never smoked', 'cat__smoking_status_smokes' ] return df[feature_columns] def predict(gender, age, hypertension, ever_married, work_type, heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type): """Make prediction using the loaded model""" if model is None: return "Error: Model not loaded" # Create input dictionary input_data = { 'gender': gender, 'age': float(age), # Ensure numeric 'hypertension': 1 if hypertension == 'Yes' else 0, 'heart_disease': 1 if heart_disease == 'Yes' else 0, 'ever_married': ever_married, 'work_type': work_type, 'Residence_type': Residence_type, 'avg_glucose_level': float(avg_glucose_level), # Ensure numeric 'bmi': float(bmi), # Ensure numeric 'smoking_status': smoking_status } # Preprocess the input processed_input = preprocess_input(input_data) # Use the loaded model try: prediction = model.predict_proba(processed_input)[0][1] return f"The probability of stroke is {prediction:.2%}" except Exception as e: return f"Error making prediction: {str(e)}, model is not valid" # updated here # Create the Gradio interface iface = gr.Interface( fn=predict, inputs=[ gr.Radio(choices=['Female', 'Male'], label="Gender"), gr.Slider(minimum=0, maximum=100, label="Age"), gr.Radio(choices=['Yes', 'No'], label="Hypertension"), gr.Radio(choices=['Yes', 'No'], label="Ever Married"), gr.Radio(choices=['Private', 'Self-employed', 'Govt_job', 'children', 'Never_worked'], label="Work Type"), gr.Radio(choices=['Yes', 'No'], label="Heart Disease"), gr.Number(label="Average Glucose Level"), gr.Slider(minimum=10, maximum=50, label="BMI"), gr.Radio(choices=['formerly smoked', 'never smoked', 'smokes', 'Unknown'], label="Smoking Status"), gr.Radio(choices=['Urban', 'Rural'], label="Residence Type") ], outputs='text', title='Stroke Probability Predictor', description='Predicts the probability of having a stroke based on input features.' ) if __name__ == "__main__": iface.launch()